Recent changes in the tax code and the ability of individuals to manage the investment of their retirement accounts have created many opportunities for maximizing growth over time, but have also introduced a new level of complexity into individual investment decision. Determining the best investments and strategy is a daunting task. A fundamental problem faced by all investors and financial advisors is which account--retirement or taxable--to put each investment in with their given asset allocation. Two factors strongly influence these decisions. First is the asset classes, such as stocks and bonds (and their sub categories) each which has various advantages and disadvantages, including expected return and risk. Second is the tax status of both the investor and the investment, which typically breaks down into taxable investment accounts and tax deferred investment accounts (e.g., 401Ks and IRAs). A decision must be made about how to distribute the asset classes between the taxable accounts and tax deferred retirement accounts. For example, if a split of 60% stock (or stock funds) and 40% bonds (or bond funds) make sense for an investor (it's an appropriate asset allocation), and this investor has $75,000 in an IRA and $25,000 in a taxable brokerage account, the question is "where should the $60,000 stock and the $40,000 bond allocations be located for maximum long-term benefits?" Should he hold the stocks entirely in the retirement account or hold $25,000 in the taxable account and $35,000 in the IRA? The present investment addresses and optimizes this question.
Because of the different income and growth characteristics of investments in the various asset classes and the different ways and rates at which they are taxed, and by whom (Federal, state, local) the decision about which account to put each asset class (investment) has a very significant impact on the after-tax investment accumulation over time. Financial advisors have been forced to "optimize" as best they could by applying their knowledge of each investments' characteristics and knowledge of the tax laws to approximate what they thought would be the highest after tax accumulation for the target investment horizon. The process is very much a "seat of the pants" exercise and the outcome depends on the advisor's level of knowledge and their ability to mentally integrate and process a set of highly complex variables.
As with any uncertain process, advisors and experts disagree upon the best strategy for investment. An article by Venessa O'Connell in the Wall Street Journal (Capital-Gains Tax Cuts Mean It's Time To Review Your Tax-Deferred Strategy, WSJ Aug. 29, 1997 page C1) stated that the question of whether to keep stocks or bonds in tax-deferred accounts has been debated among the wealthy for years. The article further went on to quote Harold Evensky, a highly respected financial advisor at Evensky, Brown & Katz in Florida, who recommended that high-income investors keep stocks in taxable accounts and favor corporate bonds in tax-deferred accounts. However, this general advice may not prove optimal, as will be shown below.
While experts disagree on even the basic strategies, the typical investor often doesn't even address the issue. Financial advisors often spend too little time on the issue of investment location, thereby ending up with less than optimal investment strategies. More often than not individual investors are oblivious of the issue to their financial detriment.
Several tools and systems attempt to address this problem. Spreadsheets can be constructed to "run the numbers", but the outcome depends on the input into the system. By changing input assumptions it is possible to test different scenarios to see which combination gave the best result. While this is an improvement over the "seat of the pants" method, it is not a true optimizer.
Similarly, commercial calculators may become available which obviate the need to construct spread sheets, but perform essentially the same function as the spreadsheets. The commercial calculators calculate accumulation amounts over time based on a set of assumptions. By changing the assumptions, the user is able to test different sets of assumptions (strategies). This is an incredibly time consuming and burdensome task given the number of variables and the possible number of combinations. A large number of variables must be considered with a huge search space. For example, 80 variables will result in over 14 billion possible combinations. None of the above approaches will yield an optimum solution.
There are several other approaches for developing an optimization solution for this complex problem with varying degrees of success. A brute force approach is simply to try every possible combination and calculate the best result. This can work fine, but with real world problems, the number of combinations is so large that this approach is not practical given a reasonable time scale.
Numerical calculator optimization techniques have been used to attempt to solve optimization problems and are now available in most advanced spreadsheet programs. But these techniques have limitations. For example, they lend themselves to optimizing independent numeric inputs from which a desired output is calculated. They are less capable of optimizing problems involving sequencing or scheduling. Also, they are "exploitation" and not "exploration" techniques. This means that given a reasonable starting solution (a set of input values), the numeric optimization will converge to a near optimal solution. However, they are not capable of exploring areas of space where good solutions exist. This is because numerical optimization techniques can often get trapped in local optimal solutions. Another limitation of numerical optimization techniques is that they are not suitable if the outcome cannot be explicitly calculated. For example, when the outcome is a subjective assessment by an expert or an observed performance.
Another approach is the use linear program techniques. These can work well when optimizing the numeric parameters of a recipe type problem. However, with the particular type of optimization problem related to investment account selection, it becomes very difficult to represent the problem in terms of linear numeric parameters. Also, as the number of parameters and equations increase, the calculations and solution surface become extremely complex. Further, false optimum solutions are prevalent, with no clear indication of recognizing the false solutions.
None of these approaches help to find an optimal solution in a reasonable amount of time (both real time and computer time).